Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the challenge of drug-target prediction, AI-driven models have emerged as powerful tools, offering innovative solutions by effectively extracting features from complex biological data, accurately modeling molecular interactions, and precisely predicting potential drug-target outcomes. Traditional machine learning (ML), network-based, and advanced deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers play a pivotal role. This review systematically compiles and evaluates AI algorithms for drug- and drug combination-target predictions, highlighting their theoretical frameworks, strengths, and limitations. CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions. GCNs provide deep insights into molecular interactions via relational data, whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences. Network-based models offer a systematic perspective by integrating diverse data sources, and traditional ML efficiently handles large datasets to improve overall predictive accuracy. Collectively, these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy. This review summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations on models and algorithms for researchers engaged in biomedical research. It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

The small-molecule alkaloid halofuginone (HF) is obtained from febrifugine. Recent studies on HF have aroused widespread attention owing to its universal range of noteworthy biological activities and therapeutic functions, which range from parasite infections and fibrosis to autoimmune diseases. In particular, HF is believed to play an excellent anticancer role by suppressing the proliferation, adhesion, metastasis, and invasion of cancers. This review supports the goal of demonstrating various anticancer effects and molecular mechanisms of HF. In the studies covered in this review, the anticancer molecular mechanisms of HF mainly included transforming growth factor-β (TGF-β)/Smad-3/nuclear factor erythroid 2-related factor 2 (Nrf2), serine/threonine kinase proteins (Akt)/mechanistic target of rapamycin complex 1(mTORC1)/wingless/integrated (Wnt)/β-catenin, the exosomal microRNA-31 (miR-31)/histone deacetylase 2 (HDAC2) signaling pathway, and the interaction of the extracellular matrix (ECM) and immune cells. Notably, HF, as a novel type of adenosine triphosphate (ATP)-dependent inhibitor that is often combined with prolyl transfer RNA synthetase (ProRS) and amino acid starvation therapy (AAS) to suppress the formation of ribosome, further exerts a significant effect on the tumor microenvironment (TME). Additionally, the combination of HF with other drugs or therapies obtained universal attention. Our results showed that HF has significant potential for clinical cancer treatment.

Mitochondria play a crucial role as organelles, managing several physiological processes such as redox balance, cell metabolism, and energy synthesis. Initially, the assumption was that mitochondria primarily resided in the host cells and could exclusively transmit from oocytes to offspring by a mechanism known as vertical inheritance of mitochondria. Recent scholarly works, however, suggest that certain cell types transmit their mitochondria to other developmental cell types via a mechanism referred to as intercellular or horizontal mitochondrial transfer. This review details the process of which mitochondria are transferred across cells and explains the impact of mitochondrial transfer between cells on the efficacy and functionality of cancer cells in various cancer forms. Specifically, we review the role of mitochondria transfer in regulating cellular metabolism restoration, excess reactive oxygen species (ROS) generation, proliferation, invasion, metastasis, mitophagy activation, mitochondrial DNA (mtDNA) inheritance, immune system modulation and therapeutic resistance in cancer. Additionally, we highlight the possibility of using intercellular mitochondria transfer as a therapeutic approach to treat cancer and enhance the efficacy of cancer treatments.

Lung cancer exhibits the highest incidence and mortality rates among cancers globally, with a five-year overall survival rate alarmingly below 20%. Targeting autophagy, though a controversial therapeutic strategy, is extensively employed in clinical practice. Current research is actively pursuing various therapeutic strategies using small molecules to exploit the dual function of autophagy. Nevertheless, the pivotal question of enhancing or inhibiting autophagy in cancer therapy merits further attention. This review aims to provide a comprehensive overview of the mechanisms of autophagy in lung cancer. It also explores recent advances in targeting cytotoxic autophagy and inhibiting protective autophagy with small molecules to induce cell death in lung cancer cells. Notably, most autophagy-targeting drugs, primarily natural small molecules, have demonstrated that activating cytotoxic autophagy effectively induces cell death in lung cancer, as opposed to inhibiting protective autophagy. These insights contribute to identifying druggable targets and drug candidates for potential autophagy-related lung cancer therapies, offering promising approaches to combat this disease.